Abstract
In 2025, the Trump administration ended long-standing efforts to determine the extent to which pollution has disproportionate impacts on different communities, including overburdened and disadvantaged communities. But, at the same time, it reaffirmed its commitment to performing benefit–cost analysis to analyze the consequences of regulations. These approaches are mutually inconsistent. A proper benefit–cost analysis simply cannot be done without considering the distribution of a policy's impacts.
Generally, benefit–cost analyses are conducted based on impacts on an average member of the population—that is, a person with median characteristics. Often, however, the populations affected by government policies vary from the median in significant ways. Heterogeneity by age, race, income, and other factors is relevant to the assessment of the benefits and costs of particular policies for three principal reasons. On the benefits side, certain groups face higher levels of exposure to environmental harms, and, where the dose–response functions are nonlinear or where exposure to different pollutants results in negative synergistic effects, these groups face higher harms from exposure to an additional unit of pollution than does an average member of the population. Also, certain groups—for example, groups in which higher levels of asthma are present—have higher vulnerability to particular exposures. And, on the cost side, certain groups bear higher burdens because of differences in geography, spending patterns, and other behaviors. This Article provides a robust review of the scientific, epidemiological, and economic literature for each of these mechanisms, surveys what agencies have already done, and sets forth an agenda for future research.
Accounting for the distributional impacts of policies can lead to large differences in the assessment of benefits and costs. For example, one study found that the top 5 percent of the population most susceptible to cancer may be subject to twenty-five times more cancer risk than the average person and that the top 1 percent may face more than one hundred times that risk. As a result, a benefit–cost analysis based on impact to the average member of the population would significantly underestimate the benefits of regulatory protections, both because the actually affected population may be at higher risk and because these high outliers raise the overall population risk. This underestimate can lead to a biased analysis and economically inefficient regulations.
Citation
Richard L. Revesz et al.,
The Myth of Evenhanded Administration: Integrating Individual Heterogeneity into Benefit–Cost Analysis,
76 Duke Law Journal
223-289
(2026)
Available at: https://scholarship.law.duke.edu/dlj/vol76/iss2/2